'models' 검색 결과

Automatically extract abstract finite-state models in SMV/NuSMV format from source code (C/C++, Java, Python) for formal model checking. Use when users need to: (1) Generate SMV models from program code for verification, (2) Extract state-transition models from protocol implementations, (3) Analyze control flow and data flow to construct formal models, (4) Create models for checking safety and liveness properties, (5) Convert imperative code to declarative state machines. Particularly effective for protocol implementations, concurrent systems, and control logic with clear state transitions.

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arkcli 模型查询与基础模型服务激活能力：列出、搜索、获取火山公共基础模型详情，以及用户明确要求的开通/激活模型服务（`arkcli models activate`）；Volc 还支持 TTFT/TPOT 性能排名、延迟趋势和输入长度对比。激活已有基础模型服务不等于部署/创建 Endpoint，不得转成 `+deploy`。优先使用产品命令 `arkcli models ...`，而不是直接调用 Raw API。反触发：用户的最终目标是创建 / 部署 Endpoint 时，本 skill 承担有界的只读候选查询，owning skill 由创建路径确定：普通产品创建走 arkcli-deploy；用户显式要求 raw CRUD / CI / 无守卫的 `infer endpoint create` 走 arkcli-infer-endpoint。候选首先执行 `models search ... --size 10 --format json`；成功完整结果直接复用，空结果与捕获缺损按 reference 有界恢复，并把实时返回的 `name` 与非空 `primary_version` 组合成可直接传给 `--model` 的完整 ID，不能拉全量清单、把裸家族名当可部署 ID，或逐候选追加 `models get`。注意：查询/管理账号下自传或精调的自定义模型（`cm-xxx`）走 arkcli-custommodel。

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Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

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Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.

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hf 명령어로 Hugging Face Hub의 모델과 데이터셋을 찾고 다운로드·업로드합니다. 저장소, 실행 환경과 로컬 캐시 관리도 다룹니다.

• 모델과 데이터셋을 찾아 다운로드하기
• 로컬 파일을 Hub에 업로드하기
• 저장소 생성과 브랜치·태그 관리하기

공통 과제로 대규모 언어 모델의 지식·추론·코드 생성을 평가합니다. 모델 비교표와 학습 단계별 성적 추이를 만들고 평가 결과와 개별 답변을 저장합니다.

• MMLU와 GSM8K로 모델 평가
• 여러 모델의 성적 비교표 작성
• 학습 중 모델의 성적 추적

Hermes에서 Actual Computer로 자신의 기기에 AI 모델을 실행하도록 연결을 설정합니다. 중계 서비스와 로컬 연결, 모델 선택, 응답 확인 및 오류 점검을 다룹니다.

• Actual 키로 Hermes를 연결합니다
• 자신의 기기에서 모델을 실행합니다
• 로컬 모델을 찾아 내려받고 불러옵니다

Wi-Fi 측정 데이터로 RuView 모델을 훈련하고 평가합니다. 자세 추정, 환경 적응, GPU 훈련과 Hugging Face 모델 공개를 다룹니다.

• 카메라와 레이블 없이 자세 추정 학습하기
• 카메라와 Wi-Fi 대응 데이터로 학습하기
• 다른 방이나 환경에 모델 적응시키기

Review estimated spending, token usage, and cache hit ratios across Claude Code, Codex, and Grok using local logs. Filter by time, project, or provider, while keeping currencies separate and identifying models without pricing.

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• Reviewing estimated spending for the past seven days
• Comparing usage across providers and models
• Checking costs for a specific project or time range

Build asset loading for Three.js scenes, including 3D models, textures, and HDR images for backgrounds and reflections. Coordinate multiple files, track loading progress, reuse cached assets, and handle failures with retries or fallback files.

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• Load GLTF/GLB models into a Three.js scene
• Show loading progress across multiple assets
• Use HDR images for backgrounds and reflections

Manage Ollama models and run text generation, chat, and embeddings, which represent text as numerical vectors, through command-line requests. Check server status, inspect model details, and receive structured JSON output for agent workflows.

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• Download models and inspect their details
• Generate text or chat with a chosen model
• Convert text into numerical vectors

Inspect and edit Live2D Cubism model files from the command line. Check referenced assets, adjust motion and expression settings, compare versions, process multiple models, and prepare model files for distribution without opening Live2D Editor.

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• Check for missing assets and unused files
• Adjust motion fade times across multiple models
• Compare model versions

Send prompts to Novita AI models such as DeepSeek and GLM from the command line. Retrieve answers as readable text or structured JSON, stream responses as they arrive, and check available models and API connectivity.

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• Send questions to DeepSeek or GLM
• Display responses as they arrive
• Retrieve AI responses as JSON

Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.

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• Build node and graph classification models
• Predict links between nodes
• Train models on graphs with multiple node and connection types

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

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• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Structure Avalonia applications with ReactiveUI and Zafiro patterns for ViewModels, the objects that manage screen state and logic. Build multi-step wizards, organize navigation, report command progress, and connect screen layouts to their supporting logic.

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• Design ViewModels for screen state and logic
• Build multi-step setup wizards
• Organize navigation and discover UI sections

Turn descriptions of 3D shapes and CAD tasks into FreeCAD Python scripts. Create models with adjustable dimensions, automate repeated operations with macros, convert meshes and solids, or build custom dialogs and workbench tools.

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• Create 3D models from written descriptions
• Build models with adjustable dimensions
• Automate repeated CAD operations with macros

Apply psychology and behavioral science to marketing decisions. The skill selects relevant thinking models, explains customer behavior, and suggests ethical changes to pricing, messaging, purchase journeys, and campaign priorities.

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• Understand why customers hesitate to buy
• Review pricing plans and how prices are presented
• Simplify signup and purchase journeys

Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.

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• Compressing LLMs without calibration datasets
• Serving quantized models with vLLM or HuggingFace Transformers
• Comparing bit widths and inference backends

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

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• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

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• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

Helps configure and troubleshoot DeepSpeed for training large models across multiple devices. Covers memory optimization, parallel training, and DeepNVMe storage transfers, with examples for tuning data movement and configuring models that divide work among specialized components.

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• Configuring distributed training for large models
• Tuning tensor transfers to NVMe storage
• Setting up models with specialized expert components

Let a NanoClaw container agent call local models running in Atomic Chat. The skill adds an MCP server, a connection that exposes model listing and text generation as tools, while Claude continues to coordinate the work.

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• Delegate part of Claude's work to a local model
• List models available in Atomic Chat
• Generate an answer with a specified local model